In recent years, personality has been regarded as a valuable personal factor being incorporated into numerous tasks such as sentiment analysis and product recommendation. This has led to widespread attention to text-based personality recognition task, which aims to identify an individual's personality based on given text. Considering that ChatGPT has recently exhibited remarkable abilities on various natural language processing tasks, we provide a preliminary evaluation of ChatGPT on text-based personality recognition task for generating effective personality data. Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level. The experimental results on two representative real-world datasets reveal that ChatGPT with zero-shot chain-of-thought prompting exhibits impressive personality recognition ability and is capable to provide natural language explanations through text-based logical reasoning. Furthermore, by employing the level-oriented prompting strategy to optimize zero-shot chain-of-thought prompting, the performance gap between ChatGPT and corresponding state-of-the-art model has been narrowed even more. However, we observe that ChatGPT shows unfairness towards certain sensitive demographic attributes such as gender and age. Additionally, we discover that eliciting the personality recognition ability of ChatGPT helps improve its performance on personality-related downstream tasks such as sentiment classification and stress prediction.

翻译：摘要：近年来，人格被视为有价值的个人因素，被融入情感分析和产品推荐等众多任务中。这使得基于文本的人格识别任务备受关注，该任务旨在根据给定文本识别个体的人格。考虑到ChatGPT近期在各种自然语言处理任务中展现出卓越能力，本文对ChatGPT在基于文本的人格识别任务中生成有效人格数据的能力进行了初步评估。具体而言，我们采用多种提示策略探究ChatGPT从给定文本中识别人格的能力，特别是我们设计的面向层级的提示策略，用于引导ChatGPT在指定层级上分析给定文本。在两个代表性真实数据集上的实验结果表明，采用零样本链式思维提示的ChatGPT展现出令人印象深刻的人格识别能力，并能通过基于文本的逻辑推理提供自然语言解释。此外，通过采用面向层级的提示策略优化零样本链式思维提示，ChatGPT与相应最先进模型之间的性能差距进一步缩小。然而，我们观察到ChatGPT对某些敏感人口统计属性（如性别和年龄）表现出不公平性。此外，我们发现激发ChatGPT的人格识别能力有助于提升其在与人格相关的下游任务（如情感分类和压力预测）中的性能。